MR Image Analysis for Radiotherapy Risk Evaluation
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Solution Overview
Problem
Current methods lack effective tools for analyzing different types of tissues inside a radiation target during radiotherapy, leading to uncertainties about long-term complications such as radiation necrosis and neurological deficits.
Innovation Solution
A method involving MR image analysis, where an MR image set and radiotherapy plan are used to convert dose intensity distribution into spatial positions, select a radiation exposure region, classify voxels into clusters based on grayscale values, and calculate volumes or ratios of these clusters to assess tissue types and potential risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiotherapy is administered to treat lesions, then therapy effects are achieved, but long-term complications such as radiation necrosis and neurological deficits occur
Solution Approach 1:
The patent applies preliminary action by performing MR image analysis and tissue classification before radiotherapy treatment planning. The system pre-identifies different tissue types (normal brain tissue, tumor tissue, edema) within the radiation target volume and calculates their volumes. This preliminary characterization allows clinicians to predict potential complications and adjust the radiation plan in advance to minimize damage to normal tissues while maintaining therapeutic effectiveness.
2Measurement precision
If traditional imaging techniques are used for radiotherapy planning, then radiation rays can be located precisely, but lack of tools exists for analyzing different types of tissues inside the target
Solution Approach 1:
The patent applies segmentation by dividing the radiation target volume into distinct tissue types based on MR image signal characteristics. The system segments the target into normal brain tissue, tumor tissue, and edema regions by analyzing signal intensity values and establishing reference ranges for each tissue type. This segmentation provides detailed tissue composition information within the radiation target, enabling precise characterization of the volume of normal tissue that will be exposed to radiation.
3Object-affected harmful factors
If stereotactic radiosurgery is performed to avoid surgical invasion, then bleeding and infection risks are reduced, but complications such as chronic expanded hematoma and post-radiation cyst may occur years later
Solution Approach 1:
The patent applies feedback by using post-treatment MR imaging to monitor changes in tissue characteristics after radiotherapy. The system compares pre- and post-treatment MR images to detect early signs of complications such as radiation necrosis, edema progression, or cyst formation. By tracking these changes over time and correlating them with the initially classified tissue volumes, the system provides feedback on treatment outcomes and potential long-term complications, allowing for early intervention if needed.
Data Source
AI summary
The present disclosure provides a magnetic resonance (MR) image analysis method for a patient who underwent radiotherapy. The method includes the steps: receiving an MR image set of a patient and a dose map of a radiotherapy plan; converting the dose intensity distribution of the dose map into the relative spatial positions in the MR image set; selecting a radiation dose and a radiation exposure region, wherein the radiation exposure region has radiation intensity being equal to or higher than the radiation dose; using the radiation exposure region to determine a region of interest (ROI) in the MR image set; classifying the voxels inside the ROI of the MR image set into different clusters according to the grayscale values of the voxels inside the ROI; and calculating the volume or ratios of the different clusters inside the ROI. The present disclosure also provides a method for evaluating risks of radiotherapy.